519 research outputs found
Scalable Breadth-First Search on a GPU Cluster
On a GPU cluster, the ratio of high computing power to communication
bandwidth makes scaling breadth-first search (BFS) on a scale-free graph
extremely challenging. By separating high and low out-degree vertices, we
present an implementation with scalable computation and a model for scalable
communication for BFS and direction-optimized BFS. Our communication model uses
global reduction for high-degree vertices, and point-to-point transmission for
low-degree vertices. Leveraging the characteristics of degree separation, we
reduce the graph size to one third of the conventional edge list
representation. With several other optimizations, we observe linear weak
scaling as we increase the number of GPUs, and achieve 259.8 GTEPS on a
scale-33 Graph500 RMAT graph with 124 GPUs on the latest CORAL early access
system.Comment: 12 pages, 13 figures. To appear at IPDPS 201
Data compression for climate data
The different rates of increase for computational power and storage capabilities of supercomputers turn data storage into a technical and economical problem. Because storage capabilities are lagging behind, investments and operational costs for storage systems have increased to keep up with the supercomputers' I/O requirements. One promising approach is to reduce the amount of data that is stored. In this paper, we take a look at the impact of compression on performance and costs of high performance systems. To this end, we analyze the applicability of compression on all layers of the I/O stack, that is, main memory, network and storage. Based on the Mistral system of the German Climate Computing Center (Deutsches Klimarechenzentrum, DKRZ), we illustrate potential performance improvements and cost savings. Making use of compression on a large scale can decrease investments and operational costs by 50% without negatively impacting performance. Additionally, we present ongoing work for supporting enhanced adaptive compression in the parallel distributed file system Lustre and application-specific compression
GekkoFS: A temporary burst buffer file system for HPC applications
Many scientific fields increasingly use high-performance computing (HPC) to process and analyze massive amounts of experimental data while storage systems in today’s HPC environments have to cope with new access patterns. These patterns include many metadata operations, small I/O requests, or randomized file I/O, while general-purpose parallel file systems have been optimized for sequential shared access to large files. Burst buffer file systems create a separate file system that applications can use to store temporary data. They aggregate node-local storage available within the compute nodes or use dedicated SSD clusters and offer a peak bandwidth higher than that of the backend parallel file system without interfering with it. However, burst buffer file systems typically offer many features that a scientific application, running in isolation for a limited amount of time, does not require. We present GekkoFS, a temporary, highly-scalable file system which has been specifically optimized for the aforementioned use cases. GekkoFS provides relaxed POSIX semantics which only offers features which are actually required by most (not all) applications. GekkoFS is, therefore, able to provide scalable I/O performance and reaches millions of metadata operations already for a small number of nodes, significantly outperforming the capabilities of common parallel file systems.Peer ReviewedPostprint (author's final draft
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